Should multiple imputation be stratified by exposure group when estimating causal effects via outcome regression in observational studies?

ABSTRACT:

Background

Despite recent advances in causal inference methods, outcome regression remains the most widely used approach for estimating causal effects in epidemiological studies with a single-point exposure and outcome. Missing data are common in these studies, and complete-case analysis (CCA) and multiple imputation (MI) are two frequently used methods for handling them. In randomised controlled trials (RCTs), it has been shown that MI should be conducted separately by treatment group. In observational studies, causal inference is now understood as the task of emulating an RCT, which raises the question of whether MI should be conducted by exposure group in such studies.

Methods

We addressed this question by evaluating the performance of seven methods for handling missing d

SUBMITTER: Zhang J 

PROVIDER: S-EPMC9933305 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature